Robust passive filtering for neutral-type neural networks with time-varying discrete and unbounded distributed delays

Abstract

The passive filtering problem is studied for a class of neutral-type neural networks with time-varying discrete and unbounded distributed delays. Based on the passive theory, a sufficient condition for the existence of the robust passive filter is given. By introducing an appropriate Lyapunov–Krasovskii functional and using Jensen's inequality technique to deal with its derivative, the criterion which ensures error dynamic system to be strictly passive with dissipation γ>0 is presented in the form of nonlinear matrix inequality. In order to solve the nonlinear problem, a cone complementarity linearization (CCL) algorithm is proposed. Furthermore, when the norm-bounded parameter uncertainties appear in the class of neural networks, the corresponding robust passive filtering problem is also investigated. Three examples are given to demonstrate the effectiveness of the proposed method.